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Strong employer brand, metro location, and common QA title drive high competition.
Role requires specialized data-quality and automation skills, moderately limiting cross-industry transferability.
Multiple mandatory technical skills across automation, cloud, and data platforms create stringent candidate filters.
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Design and maintain automated testing frameworks for APIs, applications, data pipelines, and cloud services.
Ensure data quality by implementing data validation, profiling, reconciliation, monitoring, and automated controls.
Leverage AI tools and automation to improve efficiency, reliability, and scalability across data platforms, collaborating with global teams.
Experience in test automation, quality engineering, software testing, or data quality for complex applications and data platforms.
Strong automation skills including CI/CD tools such as Jenkins, GitLab CI, GitHub Actions, or Airflow.
Proficiency in Python, Node.js, SQL, and experience with cloud platforms like AWS, Azure, or GCP.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in leveraging AI-assisted engineering practices and advanced automation in quality engineering roles.
Strong background working with modern data technologies such as data lakes, Databricks, Spark, Kafka, and tools for monitoring and observability.
Comfortable collaborating across global teams with some overlap in U.S. business hours and applying infrastructure-as-code tools (Terraform, CloudFormation) is a plus.